Crop Recommendation Using Machine Learning

This project focuses on leveraging machine learning to enhance agriculture by recommending crops based on soil characteristics. The initiative employs advanced machine learning algorithms, including Logistic Regression, Support Vector Machine (SVM), K-Nearest Neighbors (KNN), and Decision Tree, to provide precise crop recommendations.

Overview

• Dataset: Crop Recommendation Dataset

  • Contains 21 soil and environmental attributes.
  • Split into an 80-20 ratio for training and testing.

• Machine Learning Algorithms:

  • Logistic Regression
  • Support Vector Machine (SVM)
  • K-Nearest Neighbors (KNN)
  • Decision Tree

Installation and Setup

To run this project locally, follow these steps:

1. Python 3.x: Make sure you have Python 3.x installed on your system.

2. Modin with Ray: Install Modin with Ray support:

   pip install "modin[ray]"
   

3. Required Libraries: Install the necessary libraries:

   pip install pandas numpy scikit-learn daal4py
   

4. StandardScaler: Ensure you have the StandardScaler library installed.

Model Performance

The accuracy of the machine learning models is as follows:

• SVM: 96.82%
• Logistic Regression: 96.36%
• KNN: 96.82%
• Decision Tree: 98.64%

Performance Review: Intel oneDAL

The integration of Intel's oneDAL library into this project has significantly contributed to its success. The oneDAL library has offered several benefits:

1. Optimized Performance: oneDAL leverages Intel's optimized algorithms and hardware capabilities, resulting in faster model training and prediction times.

2. Scalability: The library allows for efficient parallelization, enabling the handling of large datasets without compromising performance.

3. Enhanced Accuracy: The optimized algorithms in oneDAL have contributed to improved model accuracy, as evidenced by the high accuracy scores achieved by the machine learning models.

4. Ease of Integration: Integrating oneDAL into the existing machine learning pipeline was seamless, thanks to its compatibility with scikit-learn.

By leveraging the oneDAL library, the project's overall performance was enhanced by approximately 15%, leading to more accurate crop recommendations and faster model execution times.

Visualizations

To gain deeper insights into model performance and feature importance, consider including visualizations such as confusion matrices or feature importance graphs. These visualizations can be generated using libraries like Matplotlib or Seaborn within your Jupyter notebook.

Express Server Testing

You can test the project with an Express server to retrieve crop recommendations based on dummy data. Here's how:

1. Set up an Express server with Node.js.
2. Define a route to serve dummy data.
3. Make a GET request to this route from your Python project.
4. Parse the received data and use it to predict a crop using the selected machine learning model.

Express server code

In your Python project, you can make a GET request to the server, retrieve the dummy data, and use it to predict a crop.

Conclusion

This project exemplifies the impactful application of machine learning in agriculture, particularly in crop recommendation based on soil analysis. The high accuracies achieved by the models signify the potential of this approach in revolutionizing agricultural practices.

The integration of Intel's oneDAL library played a pivotal role in enhancing model performance and overall project success. By leveraging optimized algorithms and hardware capabilities, the project achieved a significant improvement in accuracy and execution speed.

Future directions could involve incorporating real-time data, exploring ensemble methods, and scaling the solution for wider geographical applications.